{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.models as models\n",
    "import torchvision.transforms as transforms\n",
    "from torchvision.transforms import RandAugment\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import time\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "from res2net_Fca import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def get_data_loader():    \n",
    "    transform_train = transforms.Compose([\n",
    "        transforms.RandomCrop(32, padding=4),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        RandAugment(),  # 使用 RandAugment\n",
    "        transforms.ToTensor(),\n",
    "        #transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "        transforms.Normalize(\n",
    "            (0.5070751592371323, 0.48654887331495095, 0.4409178433670343),\n",
    "            (0.2673342858792401, 0.2564384629170883, 0.27615047132568404)\n",
    "        )\n",
    "    ])\n",
    "    \n",
    "    transform_test = transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "    ])\n",
    "    \n",
    "\n",
    "    trainset = torchvision.datasets.CIFAR100(root='./data', train=True,\n",
    "                                             download=False, transform=transform_train)\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,\n",
    "                                              shuffle=True, num_workers=2)\n",
    "\n",
    "    testset = torchvision.datasets.CIFAR100(root='./data', train=False,\n",
    "                                            download=False, transform=transform_test)\n",
    "    testloader = torch.utils.data.DataLoader(testset, batch_size=128,\n",
    "                                             shuffle=False, num_workers=2)\n",
    "        # 实例化模型\n",
    "    return trainloader,testloader\n",
    "\n",
    "\n",
    "def plot_training_results(train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs):\n",
    "    # Convert PyTorch tensors to NumPy arrays\n",
    "    train_losses = np.array(train_losses)\n",
    "    train_top1_accs = np.array(train_top1_accs)\n",
    "    train_top5_accs = np.array(train_top5_accs)\n",
    "    val_losses = np.array(val_losses)\n",
    "    val_top1_accs = np.array(val_top1_accs)\n",
    "    val_top5_accs = np.array(val_top5_accs)\n",
    "\n",
    "    plt.figure(figsize=(15, 5))\n",
    "    \n",
    "    # 绘制损失曲线\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(train_losses, label='训练损失')\n",
    "    plt.plot(val_losses, label='验证损失')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.title('训练/验证损失曲线')\n",
    "    plt.legend()\n",
    "    \n",
    "    # 绘制准确率曲线\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(train_top1_accs, label='训练 Top-1 准确率')\n",
    "    plt.plot(val_top1_accs, label='验证 Top-1 准确率')\n",
    "    plt.plot(train_top5_accs, label='训练 Top-5 准确率')\n",
    "    plt.plot(val_top5_accs, label='验证 Top-5 准确率')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.title('训练/验证准确率曲线')\n",
    "    plt.legend()\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# 训练模型\n",
    "def train_model(model,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs=25):\n",
    "    since = time.time()\n",
    "       \n",
    "    train_losses = []\n",
    "    train_top1_accs = []\n",
    "    train_top5_accs = []\n",
    "    val_losses = []\n",
    "    val_top1_accs = []\n",
    "    val_top5_accs = []\n",
    "\n",
    "\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_acc = 0.0\n",
    " \n",
    "    for epoch in range(num_epochs):\n",
    "        print('第 {}/{} 轮训练'.format(epoch, num_epochs - 1))\n",
    "        print('-' * 10)\n",
    "\n",
    "        # 每个epoch都有训练和验证阶段\n",
    "        for phase in ['train', 'val']:\n",
    "            if phase == 'train':\n",
    "                model.train()  # 设置模型为训练模式\n",
    "            else:\n",
    "                model.eval()   # 设置模型为评估模式\n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "            running_top1_corrects = 0\n",
    "            running_top5_corrects = 0\n",
    "\n",
    "            # 遍历数据\n",
    "            for inputs, labels in (trainloader if phase == 'train' else testloader):\n",
    "                inputs = inputs.to(device)\n",
    "                labels = labels.to(device)\n",
    "\n",
    "                # 零化参数梯度\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # 前向传播\n",
    "                # 只在训练阶段追踪历史\n",
    "                with torch.set_grad_enabled(phase == 'train'):\n",
    "                    outputs = model(inputs)\n",
    "                    _, preds = torch.max(outputs, 1)\n",
    "                    loss = criterion(outputs, labels)\n",
    "\n",
    "                    # 计算 top-1 和 top-5 准确率\n",
    "                    _, top5_preds = torch.topk(outputs, 5, dim=1)\n",
    "                    top1_correct = torch.sum(preds == labels.data)\n",
    "                    top5_correct = torch.sum(top5_preds == labels.view(-1, 1))\n",
    "\n",
    "                    # 只有在训练阶段反向传播+优化\n",
    "                    if phase == 'train':\n",
    "                        loss.backward()\n",
    "                        optimizer.step()\n",
    "\n",
    "                # 统计\n",
    "                running_loss += loss.item() * inputs.size(0)\n",
    "                running_corrects += top1_correct\n",
    "                running_top1_corrects += top1_correct\n",
    "                running_top5_corrects += top5_correct\n",
    "\n",
    "            if phase == 'train':\n",
    "                scheduler.step()\n",
    "\n",
    "            epoch_loss = running_loss / len(trainloader.dataset if phase == 'train' else testloader.dataset)\n",
    "            epoch_top1_acc = running_top1_corrects.double() / len(trainloader.dataset if phase == 'train' else testloader.dataset)\n",
    "            epoch_top5_acc = running_top5_corrects.double() / len(trainloader.dataset if phase == 'train' else testloader.dataset)\n",
    "\n",
    "            print('{} 损失: {:.4f} Top-1 准确率: {:.4f} Top-5 准确率: {:.4f}'.format(\n",
    "                phase, epoch_loss, epoch_top1_acc, epoch_top5_acc))\n",
    "            \n",
    "            # 保存损失和准确率\n",
    "            if phase == 'train':\n",
    "                train_losses.append(epoch_loss)\n",
    "                train_top1_accs.append(epoch_top1_acc)\n",
    "                train_top5_accs.append(epoch_top5_acc)\n",
    "            else:\n",
    "                val_losses.append(epoch_loss)\n",
    "                val_top1_accs.append(epoch_top1_acc)\n",
    "                val_top5_accs.append(epoch_top5_acc)\n",
    "\n",
    "            # 深度复制模型\n",
    "            if phase == 'val' and epoch_top1_acc > best_acc:\n",
    "                best_acc = epoch_top1_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "\n",
    "        print()\n",
    "\n",
    "    time_elapsed = time.time() - since\n",
    "    print('训练完成，耗时 {:.0f}m {:.0f}s'.format(\n",
    "        time_elapsed // 60, time_elapsed % 60))\n",
    "    print('最佳验证集准确率: {:4f}'.format(best_acc))\n",
    "\n",
    "    # 加载最佳模型权重\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    \n",
    "    # 绘制训练结果曲线\n",
    "\n",
    "    return model,(train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "第 0/99 轮训练\n",
      "----------\n",
      "train 损失: 4.2162 Top-1 准确率: 0.0940 Top-5 准确率: 0.2740\n",
      "val 损失: 4.1055 Top-1 准确率: 0.1007 Top-5 准确率: 0.2903\n",
      "\n",
      "第 1/99 轮训练\n",
      "----------\n",
      "train 损失: 3.5506 Top-1 准确率: 0.1699 Top-5 准确率: 0.4181\n",
      "val 损失: 3.6280 Top-1 准确率: 0.1740 Top-5 准确率: 0.4165\n",
      "\n",
      "第 2/99 轮训练\n",
      "----------\n",
      "train 损失: 3.2647 Top-1 准确率: 0.2172 Top-5 准确率: 0.4912\n",
      "val 损失: 3.4013 Top-1 准确率: 0.2100 Top-5 准确率: 0.4731\n",
      "\n",
      "第 3/99 轮训练\n",
      "----------\n",
      "train 损失: 3.0770 Top-1 准确率: 0.2479 Top-5 准确率: 0.5397\n",
      "val 损失: 3.1018 Top-1 准确率: 0.2520 Top-5 准确率: 0.5353\n",
      "\n",
      "第 4/99 轮训练\n",
      "----------\n",
      "train 损失: 2.9427 Top-1 准确率: 0.2712 Top-5 准确率: 0.5706\n",
      "val 损失: 3.1561 Top-1 准确率: 0.2483 Top-5 准确率: 0.5260\n",
      "\n",
      "第 5/99 轮训练\n",
      "----------\n",
      "train 损失: 2.8321 Top-1 准确率: 0.2950 Top-5 准确率: 0.5938\n",
      "val 损失: 3.0180 Top-1 准确率: 0.2666 Top-5 准确率: 0.5612\n",
      "\n",
      "第 6/99 轮训练\n",
      "----------\n",
      "train 损失: 2.7419 Top-1 准确率: 0.3117 Top-5 准确率: 0.6150\n",
      "val 损失: 2.9772 Top-1 准确率: 0.2766 Top-5 准确率: 0.5643\n",
      "\n",
      "第 7/99 轮训练\n",
      "----------\n",
      "train 损失: 2.6622 Top-1 准确率: 0.3253 Top-5 准确率: 0.6357\n",
      "val 损失: 2.7948 Top-1 准确率: 0.3129 Top-5 准确率: 0.6054\n",
      "\n",
      "第 8/99 轮训练\n",
      "----------\n",
      "train 损失: 2.5982 Top-1 准确率: 0.3399 Top-5 准确率: 0.6491\n",
      "val 损失: 2.7602 Top-1 准确率: 0.3187 Top-5 准确率: 0.6143\n",
      "\n",
      "第 9/99 轮训练\n",
      "----------\n",
      "train 损失: 2.5453 Top-1 准确率: 0.3532 Top-5 准确率: 0.6610\n",
      "val 损失: 2.6257 Top-1 准确率: 0.3431 Top-5 准确率: 0.6416\n",
      "\n",
      "第 10/99 轮训练\n",
      "----------\n",
      "train 损失: 2.4840 Top-1 准确率: 0.3630 Top-5 准确率: 0.6751\n",
      "val 损失: 2.6173 Top-1 准确率: 0.3417 Top-5 准确率: 0.6476\n",
      "\n",
      "第 11/99 轮训练\n",
      "----------\n",
      "train 损失: 2.4398 Top-1 准确率: 0.3724 Top-5 准确率: 0.6829\n",
      "val 损失: 2.6725 Top-1 准确率: 0.3239 Top-5 准确率: 0.6360\n",
      "\n",
      "第 12/99 轮训练\n",
      "----------\n",
      "train 损失: 2.4098 Top-1 准确率: 0.3773 Top-5 准确率: 0.6905\n",
      "val 损失: 2.6426 Top-1 准确率: 0.3442 Top-5 准确率: 0.6392\n",
      "\n",
      "第 13/99 轮训练\n",
      "----------\n",
      "train 损失: 2.3598 Top-1 准确率: 0.3887 Top-5 准确率: 0.6991\n",
      "val 损失: 2.5454 Top-1 准确率: 0.3617 Top-5 准确率: 0.6649\n",
      "\n",
      "第 14/99 轮训练\n",
      "----------\n",
      "train 损失: 2.3304 Top-1 准确率: 0.3929 Top-5 准确率: 0.7058\n",
      "val 损失: 2.5080 Top-1 准确率: 0.3657 Top-5 准确率: 0.6677\n",
      "\n",
      "第 15/99 轮训练\n",
      "----------\n",
      "train 损失: 2.2974 Top-1 准确率: 0.4009 Top-5 准确率: 0.7133\n",
      "val 损失: 2.5804 Top-1 准确率: 0.3515 Top-5 准确率: 0.6632\n",
      "\n",
      "第 16/99 轮训练\n",
      "----------\n",
      "train 损失: 2.2647 Top-1 准确率: 0.4061 Top-5 准确率: 0.7187\n",
      "val 损失: 2.5918 Top-1 准确率: 0.3579 Top-5 准确率: 0.6506\n",
      "\n",
      "第 17/99 轮训练\n",
      "----------\n",
      "train 损失: 2.2289 Top-1 准确率: 0.4186 Top-5 准确率: 0.7247\n",
      "val 损失: 2.4690 Top-1 准确率: 0.3758 Top-5 准确率: 0.6772\n",
      "\n",
      "第 18/99 轮训练\n",
      "----------\n",
      "train 损失: 2.2084 Top-1 准确率: 0.4213 Top-5 准确率: 0.7303\n",
      "val 损失: 2.3672 Top-1 准确率: 0.3976 Top-5 准确率: 0.6933\n",
      "\n",
      "第 19/99 轮训练\n",
      "----------\n",
      "train 损失: 2.1802 Top-1 准确率: 0.4253 Top-5 准确率: 0.7366\n",
      "val 损失: 2.4986 Top-1 准确率: 0.3703 Top-5 准确率: 0.6687\n",
      "\n",
      "第 20/99 轮训练\n",
      "----------\n",
      "train 损失: 2.1533 Top-1 准确率: 0.4315 Top-5 准确率: 0.7419\n",
      "val 损失: 2.3977 Top-1 准确率: 0.3894 Top-5 准确率: 0.6918\n",
      "\n",
      "第 21/99 轮训练\n",
      "----------\n",
      "train 损失: 2.1358 Top-1 准确率: 0.4350 Top-5 准确率: 0.7448\n",
      "val 损失: 2.3624 Top-1 准确率: 0.3936 Top-5 准确率: 0.6964\n",
      "\n",
      "第 22/99 轮训练\n",
      "----------\n",
      "train 损失: 2.1107 Top-1 准确率: 0.4403 Top-5 准确率: 0.7486\n",
      "val 损失: 2.3239 Top-1 准确率: 0.4043 Top-5 准确率: 0.7097\n",
      "\n",
      "第 23/99 轮训练\n",
      "----------\n",
      "train 损失: 2.0973 Top-1 准确率: 0.4435 Top-5 准确率: 0.7528\n",
      "val 损失: 2.3638 Top-1 准确率: 0.3953 Top-5 准确率: 0.6996\n",
      "\n",
      "第 24/99 轮训练\n",
      "----------\n",
      "train 损失: 2.0735 Top-1 准确率: 0.4515 Top-5 准确率: 0.7558\n",
      "val 损失: 2.5709 Top-1 准确率: 0.3650 Top-5 准确率: 0.6565\n",
      "\n",
      "第 25/99 轮训练\n",
      "----------\n",
      "train 损失: 2.0461 Top-1 准确率: 0.4556 Top-5 准确率: 0.7617\n",
      "val 损失: 2.2868 Top-1 准确率: 0.4108 Top-5 准确率: 0.7131\n",
      "\n",
      "第 26/99 轮训练\n",
      "----------\n",
      "train 损失: 2.0371 Top-1 准确率: 0.4601 Top-5 准确率: 0.7631\n",
      "val 损失: 2.3020 Top-1 准确率: 0.4060 Top-5 准确率: 0.7109\n",
      "\n",
      "第 27/99 轮训练\n",
      "----------\n",
      "train 损失: 2.0232 Top-1 准确率: 0.4629 Top-5 准确率: 0.7663\n",
      "val 损失: 2.3126 Top-1 准确率: 0.4123 Top-5 准确率: 0.7094\n",
      "\n",
      "第 28/99 轮训练\n",
      "----------\n",
      "train 损失: 2.0072 Top-1 准确率: 0.4642 Top-5 准确率: 0.7694\n",
      "val 损失: 2.2543 Top-1 准确率: 0.4241 Top-5 准确率: 0.7215\n",
      "\n",
      "第 29/99 轮训练\n",
      "----------\n",
      "train 损失: 1.9960 Top-1 准确率: 0.4684 Top-5 准确率: 0.7715\n",
      "val 损失: 2.2599 Top-1 准确率: 0.4219 Top-5 准确率: 0.7183\n",
      "\n",
      "第 30/99 轮训练\n",
      "----------\n",
      "train 损失: 1.9843 Top-1 准确率: 0.4720 Top-5 准确率: 0.7740\n",
      "val 损失: 2.3621 Top-1 准确率: 0.4049 Top-5 准确率: 0.6995\n",
      "\n",
      "第 31/99 轮训练\n",
      "----------\n",
      "train 损失: 1.9628 Top-1 准确率: 0.4753 Top-5 准确率: 0.7779\n",
      "val 损失: 2.1879 Top-1 准确率: 0.4281 Top-5 准确率: 0.7336\n",
      "\n",
      "第 32/99 轮训练\n",
      "----------\n",
      "train 损失: 1.9540 Top-1 准确率: 0.4761 Top-5 准确率: 0.7794\n",
      "val 损失: 2.3163 Top-1 准确率: 0.4127 Top-5 准确率: 0.7115\n",
      "\n",
      "第 33/99 轮训练\n",
      "----------\n",
      "train 损失: 1.9313 Top-1 准确率: 0.4805 Top-5 准确率: 0.7839\n",
      "val 损失: 2.3676 Top-1 准确率: 0.4032 Top-5 准确率: 0.7019\n",
      "\n",
      "第 34/99 轮训练\n",
      "----------\n",
      "train 损失: 1.9322 Top-1 准确率: 0.4819 Top-5 准确率: 0.7837\n",
      "val 损失: 2.1762 Top-1 准确率: 0.4363 Top-5 准确率: 0.7430\n",
      "\n",
      "第 35/99 轮训练\n",
      "----------\n",
      "train 损失: 1.9064 Top-1 准确率: 0.4888 Top-5 准确率: 0.7880\n",
      "val 损失: 2.5102 Top-1 准确率: 0.3820 Top-5 准确率: 0.6797\n",
      "\n",
      "第 36/99 轮训练\n",
      "----------\n",
      "train 损失: 1.9058 Top-1 准确率: 0.4881 Top-5 准确率: 0.7898\n",
      "val 损失: 2.2420 Top-1 准确率: 0.4311 Top-5 准确率: 0.7241\n",
      "\n",
      "第 37/99 轮训练\n",
      "----------\n",
      "train 损失: 1.8880 Top-1 准确率: 0.4929 Top-5 准确率: 0.7913\n",
      "val 损失: 2.2800 Top-1 准确率: 0.4227 Top-5 准确率: 0.7139\n",
      "\n",
      "第 38/99 轮训练\n",
      "----------\n",
      "train 损失: 1.8868 Top-1 准确率: 0.4934 Top-5 准确率: 0.7920\n",
      "val 损失: 2.2643 Top-1 准确率: 0.4268 Top-5 准确率: 0.7159\n",
      "\n",
      "第 39/99 轮训练\n",
      "----------\n",
      "train 损失: 1.8795 Top-1 准确率: 0.4948 Top-5 准确率: 0.7922\n",
      "val 损失: 2.1721 Top-1 准确率: 0.4371 Top-5 准确率: 0.7404\n",
      "\n",
      "第 40/99 轮训练\n",
      "----------\n",
      "train 损失: 1.8677 Top-1 准确率: 0.4951 Top-5 准确率: 0.7946\n",
      "val 损失: 2.2620 Top-1 准确率: 0.4205 Top-5 准确率: 0.7226\n",
      "\n",
      "第 41/99 轮训练\n",
      "----------\n",
      "train 损失: 1.8521 Top-1 准确率: 0.4980 Top-5 准确率: 0.7981\n",
      "val 损失: 2.3633 Top-1 准确率: 0.4089 Top-5 准确率: 0.7013\n",
      "\n",
      "第 42/99 轮训练\n",
      "----------\n",
      "train 损失: 1.8455 Top-1 准确率: 0.5028 Top-5 准确率: 0.8009\n",
      "val 损失: 2.2682 Top-1 准确率: 0.4175 Top-5 准确率: 0.7209\n",
      "\n",
      "第 43/99 轮训练\n",
      "----------\n",
      "train 损失: 1.8404 Top-1 准确率: 0.5008 Top-5 准确率: 0.8018\n",
      "val 损失: 2.1618 Top-1 准确率: 0.4421 Top-5 准确率: 0.7416\n",
      "\n",
      "第 44/99 轮训练\n",
      "----------\n",
      "train 损失: 1.8313 Top-1 准确率: 0.5041 Top-5 准确率: 0.8006\n",
      "val 损失: 2.0954 Top-1 准确率: 0.4507 Top-5 准确率: 0.7517\n",
      "\n",
      "第 45/99 轮训练\n",
      "----------\n",
      "train 损失: 1.8119 Top-1 准确率: 0.5081 Top-5 准确率: 0.8048\n",
      "val 损失: 2.2592 Top-1 准确率: 0.4211 Top-5 准确率: 0.7224\n",
      "\n",
      "第 46/99 轮训练\n",
      "----------\n",
      "train 损失: 1.8181 Top-1 准确率: 0.5065 Top-5 准确率: 0.8039\n",
      "val 损失: 2.2459 Top-1 准确率: 0.4265 Top-5 准确率: 0.7291\n",
      "\n",
      "第 47/99 轮训练\n",
      "----------\n",
      "train 损失: 1.8096 Top-1 准确率: 0.5079 Top-5 准确率: 0.8058\n",
      "val 损失: 2.1332 Top-1 准确率: 0.4463 Top-5 准确率: 0.7436\n",
      "\n",
      "第 48/99 轮训练\n",
      "----------\n",
      "train 损失: 1.7966 Top-1 准确率: 0.5135 Top-5 准确率: 0.8086\n",
      "val 损失: 2.1954 Top-1 准确率: 0.4435 Top-5 准确率: 0.7413\n",
      "\n",
      "第 49/99 轮训练\n",
      "----------\n",
      "train 损失: 1.8003 Top-1 准确率: 0.5104 Top-5 准确率: 0.8072\n",
      "val 损失: 2.1315 Top-1 准确率: 0.4445 Top-5 准确率: 0.7509\n",
      "\n",
      "第 50/99 轮训练\n",
      "----------\n",
      "train 损失: 1.5284 Top-1 准确率: 0.5791 Top-5 准确率: 0.8520\n",
      "val 损失: 1.8190 Top-1 准确率: 0.5151 Top-5 准确率: 0.8021\n",
      "\n",
      "第 51/99 轮训练\n",
      "----------\n",
      "train 损失: 1.4222 Top-1 准确率: 0.6091 Top-5 准确率: 0.8694\n",
      "val 损失: 1.8075 Top-1 准确率: 0.5201 Top-5 准确率: 0.8033\n",
      "\n",
      "第 52/99 轮训练\n",
      "----------\n",
      "train 损失: 1.3904 Top-1 准确率: 0.6173 Top-5 准确率: 0.8719\n",
      "val 损失: 1.7626 Top-1 准确率: 0.5282 Top-5 准确率: 0.8119\n",
      "\n",
      "第 53/99 轮训练\n",
      "----------\n",
      "train 损失: 1.3655 Top-1 准确率: 0.6229 Top-5 准确率: 0.8770\n",
      "val 损失: 1.7714 Top-1 准确率: 0.5307 Top-5 准确率: 0.8064\n",
      "\n",
      "第 54/99 轮训练\n",
      "----------\n",
      "train 损失: 1.3467 Top-1 准确率: 0.6292 Top-5 准确率: 0.8808\n",
      "val 损失: 1.7721 Top-1 准确率: 0.5295 Top-5 准确率: 0.8091\n",
      "\n",
      "第 55/99 轮训练\n",
      "----------\n",
      "train 损失: 1.3354 Top-1 准确率: 0.6270 Top-5 准确率: 0.8807\n",
      "val 损失: 1.7956 Top-1 准确率: 0.5261 Top-5 准确率: 0.8014\n",
      "\n",
      "第 56/99 轮训练\n",
      "----------\n",
      "train 损失: 1.3209 Top-1 准确率: 0.6328 Top-5 准确率: 0.8814\n",
      "val 损失: 1.7853 Top-1 准确率: 0.5305 Top-5 准确率: 0.8064\n",
      "\n",
      "第 57/99 轮训练\n",
      "----------\n",
      "train 损失: 1.3085 Top-1 准确率: 0.6359 Top-5 准确率: 0.8846\n",
      "val 损失: 1.7534 Top-1 准确率: 0.5375 Top-5 准确率: 0.8114\n",
      "\n",
      "第 58/99 轮训练\n",
      "----------\n",
      "train 损失: 1.3028 Top-1 准确率: 0.6367 Top-5 准确率: 0.8839\n",
      "val 损失: 1.7438 Top-1 准确率: 0.5338 Top-5 准确率: 0.8129\n",
      "\n",
      "第 59/99 轮训练\n",
      "----------\n",
      "train 损失: 1.2946 Top-1 准确率: 0.6388 Top-5 准确率: 0.8855\n",
      "val 损失: 1.7706 Top-1 准确率: 0.5317 Top-5 准确率: 0.8089\n",
      "\n",
      "第 60/99 轮训练\n",
      "----------\n",
      "train 损失: 1.2874 Top-1 准确率: 0.6414 Top-5 准确率: 0.8869\n",
      "val 损失: 1.7479 Top-1 准确率: 0.5400 Top-5 准确率: 0.8122\n",
      "\n",
      "第 61/99 轮训练\n",
      "----------\n",
      "train 损失: 1.2883 Top-1 准确率: 0.6396 Top-5 准确率: 0.8869\n",
      "val 损失: 1.7488 Top-1 准确率: 0.5373 Top-5 准确率: 0.8126\n",
      "\n",
      "第 62/99 轮训练\n",
      "----------\n",
      "train 损失: 1.2698 Top-1 准确率: 0.6452 Top-5 准确率: 0.8913\n",
      "val 损失: 1.7994 Top-1 准确率: 0.5251 Top-5 准确率: 0.8043\n",
      "\n",
      "第 63/99 轮训练\n",
      "----------\n",
      "train 损失: 1.2696 Top-1 准确率: 0.6422 Top-5 准确率: 0.8891\n",
      "val 损失: 1.7681 Top-1 准确率: 0.5362 Top-5 准确率: 0.8075\n",
      "\n",
      "第 64/99 轮训练\n",
      "----------\n",
      "train 损失: 1.2556 Top-1 准确率: 0.6500 Top-5 准确率: 0.8918\n",
      "val 损失: 1.7207 Top-1 准确率: 0.5447 Top-5 准确率: 0.8169\n",
      "\n",
      "第 65/99 轮训练\n",
      "----------\n",
      "train 损失: 1.2459 Top-1 准确率: 0.6512 Top-5 准确率: 0.8939\n",
      "val 损失: 1.7689 Top-1 准确率: 0.5365 Top-5 准确率: 0.8089\n",
      "\n",
      "第 66/99 轮训练\n",
      "----------\n",
      "train 损失: 1.2398 Top-1 准确率: 0.6525 Top-5 准确率: 0.8939\n",
      "val 损失: 1.7298 Top-1 准确率: 0.5407 Top-5 准确率: 0.8187\n",
      "\n",
      "第 67/99 轮训练\n",
      "----------\n",
      "train 损失: 1.2313 Top-1 准确率: 0.6532 Top-5 准确率: 0.8968\n",
      "val 损失: 1.7428 Top-1 准确率: 0.5401 Top-5 准确率: 0.8131\n",
      "\n",
      "第 68/99 轮训练\n",
      "----------\n",
      "train 损失: 1.2410 Top-1 准确率: 0.6528 Top-5 准确率: 0.8946\n",
      "val 损失: 1.7605 Top-1 准确率: 0.5402 Top-5 准确率: 0.8099\n",
      "\n",
      "第 69/99 轮训练\n",
      "----------\n",
      "train 损失: 1.2175 Top-1 准确率: 0.6596 Top-5 准确率: 0.8974\n",
      "val 损失: 1.7502 Top-1 准确率: 0.5373 Top-5 准确率: 0.8146\n",
      "\n",
      "第 70/99 轮训练\n",
      "----------\n",
      "train 损失: 1.2193 Top-1 准确率: 0.6585 Top-5 准确率: 0.8981\n",
      "val 损失: 1.7607 Top-1 准确率: 0.5337 Top-5 准确率: 0.8118\n",
      "\n",
      "第 71/99 轮训练\n",
      "----------\n",
      "train 损失: 1.2145 Top-1 准确率: 0.6595 Top-5 准确率: 0.8977\n",
      "val 损失: 1.7141 Top-1 准确率: 0.5485 Top-5 准确率: 0.8167\n",
      "\n",
      "第 72/99 轮训练\n",
      "----------\n",
      "train 损失: 1.2050 Top-1 准确率: 0.6618 Top-5 准确率: 0.8989\n",
      "val 损失: 1.7611 Top-1 准确率: 0.5362 Top-5 准确率: 0.8092\n",
      "\n",
      "第 73/99 轮训练\n",
      "----------\n",
      "train 损失: 1.1960 Top-1 准确率: 0.6628 Top-5 准确率: 0.8998\n",
      "val 损失: 1.7480 Top-1 准确率: 0.5398 Top-5 准确率: 0.8131\n",
      "\n",
      "第 74/99 轮训练\n",
      "----------\n",
      "train 损失: 1.2073 Top-1 准确率: 0.6616 Top-5 准确率: 0.8981\n",
      "val 损失: 1.7586 Top-1 准确率: 0.5363 Top-5 准确率: 0.8127\n",
      "\n",
      "第 75/99 轮训练\n",
      "----------\n",
      "train 损失: 1.1862 Top-1 准确率: 0.6665 Top-5 准确率: 0.9036\n",
      "val 损失: 1.7062 Top-1 准确率: 0.5515 Top-5 准确率: 0.8197\n",
      "\n",
      "第 76/99 轮训练\n",
      "----------\n",
      "train 损失: 1.1882 Top-1 准确率: 0.6653 Top-5 准确率: 0.9015\n",
      "val 损失: 1.7217 Top-1 准确率: 0.5490 Top-5 准确率: 0.8174\n",
      "\n",
      "第 77/99 轮训练\n",
      "----------\n",
      "train 损失: 1.1790 Top-1 准确率: 0.6669 Top-5 准确率: 0.9030\n",
      "val 损失: 1.7171 Top-1 准确率: 0.5444 Top-5 准确率: 0.8186\n",
      "\n",
      "第 78/99 轮训练\n",
      "----------\n",
      "train 损失: 1.1815 Top-1 准确率: 0.6677 Top-5 准确率: 0.9019\n",
      "val 损失: 1.7654 Top-1 准确率: 0.5390 Top-5 准确率: 0.8116\n",
      "\n",
      "第 79/99 轮训练\n",
      "----------\n",
      "train 损失: 1.1726 Top-1 准确率: 0.6699 Top-5 准确率: 0.9045\n",
      "val 损失: 1.7137 Top-1 准确率: 0.5486 Top-5 准确率: 0.8194\n",
      "\n",
      "第 80/99 轮训练\n",
      "----------\n",
      "train 损失: 1.1607 Top-1 准确率: 0.6699 Top-5 准确率: 0.9073\n",
      "val 损失: 1.7239 Top-1 准确率: 0.5492 Top-5 准确率: 0.8174\n",
      "\n",
      "第 81/99 轮训练\n",
      "----------\n",
      "train 损失: 1.1511 Top-1 准确率: 0.6747 Top-5 准确率: 0.9060\n",
      "val 损失: 1.7408 Top-1 准确率: 0.5421 Top-5 准确率: 0.8162\n",
      "\n",
      "第 82/99 轮训练\n",
      "----------\n",
      "train 损失: 1.1608 Top-1 准确率: 0.6708 Top-5 准确率: 0.9051\n",
      "val 损失: 1.7659 Top-1 准确率: 0.5403 Top-5 准确率: 0.8129\n",
      "\n",
      "第 83/99 轮训练\n",
      "----------\n",
      "train 损失: 1.1538 Top-1 准确率: 0.6727 Top-5 准确率: 0.9066\n",
      "val 损失: 1.7529 Top-1 准确率: 0.5428 Top-5 准确率: 0.8180\n",
      "\n",
      "第 84/99 轮训练\n",
      "----------\n",
      "train 损失: 1.1433 Top-1 准确率: 0.6760 Top-5 准确率: 0.9075\n",
      "val 损失: 1.7285 Top-1 准确率: 0.5482 Top-5 准确率: 0.8193\n",
      "\n",
      "第 85/99 轮训练\n",
      "----------\n",
      "train 损失: 1.1461 Top-1 准确率: 0.6753 Top-5 准确率: 0.9090\n",
      "val 损失: 1.8378 Top-1 准确率: 0.5241 Top-5 准确率: 0.8030\n",
      "\n",
      "第 86/99 轮训练\n",
      "----------\n",
      "train 损失: 1.1452 Top-1 准确率: 0.6762 Top-5 准确率: 0.9078\n",
      "val 损失: 1.7888 Top-1 准确率: 0.5353 Top-5 准确率: 0.8094\n",
      "\n",
      "第 87/99 轮训练\n",
      "----------\n",
      "train 损失: 1.1350 Top-1 准确率: 0.6794 Top-5 准确率: 0.9091\n",
      "val 损失: 1.7812 Top-1 准确率: 0.5418 Top-5 准确率: 0.8103\n",
      "\n",
      "第 88/99 轮训练\n",
      "----------\n",
      "train 损失: 1.1310 Top-1 准确率: 0.6794 Top-5 准确率: 0.9088\n",
      "val 损失: 1.7378 Top-1 准确率: 0.5493 Top-5 准确率: 0.8164\n",
      "\n",
      "第 89/99 轮训练\n",
      "----------\n",
      "train 损失: 1.1211 Top-1 准确率: 0.6830 Top-5 准确率: 0.9114\n",
      "val 损失: 1.7270 Top-1 准确率: 0.5522 Top-5 准确率: 0.8200\n",
      "\n",
      "第 90/99 轮训练\n",
      "----------\n",
      "train 损失: 1.1187 Top-1 准确率: 0.6816 Top-5 准确率: 0.9132\n",
      "val 损失: 1.7688 Top-1 准确率: 0.5402 Top-5 准确率: 0.8133\n",
      "\n",
      "第 91/99 轮训练\n",
      "----------\n",
      "train 损失: 1.1192 Top-1 准确率: 0.6828 Top-5 准确率: 0.9131\n",
      "val 损失: 1.7880 Top-1 准确率: 0.5340 Top-5 准确率: 0.8147\n",
      "\n",
      "第 92/99 轮训练\n",
      "----------\n",
      "train 损失: 1.1145 Top-1 准确率: 0.6829 Top-5 准确率: 0.9128\n",
      "val 损失: 1.7384 Top-1 准确率: 0.5449 Top-5 准确率: 0.8191\n",
      "\n",
      "第 93/99 轮训练\n",
      "----------\n",
      "train 损失: 1.1030 Top-1 准确率: 0.6860 Top-5 准确率: 0.9146\n",
      "val 损失: 1.7352 Top-1 准确率: 0.5520 Top-5 准确率: 0.8174\n",
      "\n",
      "第 94/99 轮训练\n",
      "----------\n",
      "train 损失: 1.1074 Top-1 准确率: 0.6855 Top-5 准确率: 0.9137\n",
      "val 损失: 1.7776 Top-1 准确率: 0.5353 Top-5 准确率: 0.8129\n",
      "\n",
      "第 95/99 轮训练\n",
      "----------\n",
      "train 损失: 1.1009 Top-1 准确率: 0.6881 Top-5 准确率: 0.9125\n",
      "val 损失: 1.7870 Top-1 准确率: 0.5409 Top-5 准确率: 0.8103\n",
      "\n",
      "第 96/99 轮训练\n",
      "----------\n",
      "train 损失: 1.0998 Top-1 准确率: 0.6892 Top-5 准确率: 0.9134\n",
      "val 损失: 1.7687 Top-1 准确率: 0.5410 Top-5 准确率: 0.8188\n",
      "\n",
      "第 97/99 轮训练\n",
      "----------\n",
      "train 损失: 1.0955 Top-1 准确率: 0.6878 Top-5 准确率: 0.9154\n",
      "val 损失: 1.7935 Top-1 准确率: 0.5319 Top-5 准确率: 0.8136\n",
      "\n",
      "第 98/99 轮训练\n",
      "----------\n",
      "train 损失: 1.0916 Top-1 准确率: 0.6881 Top-5 准确率: 0.9141\n",
      "val 损失: 1.7515 Top-1 准确率: 0.5491 Top-5 准确率: 0.8155\n",
      "\n",
      "第 99/99 轮训练\n",
      "----------\n",
      "train 损失: 1.0890 Top-1 准确率: 0.6910 Top-5 准确率: 0.9152\n",
      "val 损失: 1.7481 Top-1 准确率: 0.5480 Top-5 准确率: 0.8161\n",
      "\n",
      "训练完成，耗时 58m 40s\n",
      "最佳验证集准确率: 0.552200\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    " ## 修改模型\n",
    "net = res2net18_fca().to(device)\n",
    "num_epochs=100\n",
    " # 定义损失函数和优化器\n",
    "trainloader,testloader = get_data_loader()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)\n",
    "scheduler = lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1)\n",
    "model,(train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs) = train_model(net,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs)  \n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_cpu_numpy(tensor):\n",
    "    return tensor.cpu().numpy()\n",
    "def to_cpu(list_):\n",
    "    if isinstance(list_, list):\n",
    "        return list(map(to_cpu_numpy,list_))\n",
    "    else:\n",
    "        return to_cpu_numpy(list_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zhaokangming\\AppData\\Local\\Temp\\ipykernel_8484\\1235040010.py:64: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\zhaokangming\\AppData\\Local\\Temp\\ipykernel_8484\\1235040010.py:64: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\zhaokangming\\AppData\\Local\\Temp\\ipykernel_8484\\1235040010.py:64: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\zhaokangming\\AppData\\Local\\Temp\\ipykernel_8484\\1235040010.py:64: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\zhaokangming\\AppData\\Local\\Temp\\ipykernel_8484\\1235040010.py:64: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\zhaokangming\\AppData\\Local\\Temp\\ipykernel_8484\\1235040010.py:64: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\zhaokangming\\AppData\\Local\\Temp\\ipykernel_8484\\1235040010.py:64: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\zhaokangming\\AppData\\Local\\Temp\\ipykernel_8484\\1235040010.py:64: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\zhaokangming\\AppData\\Local\\Temp\\ipykernel_8484\\1235040010.py:64: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\zhaokangming\\AppData\\Local\\Temp\\ipykernel_8484\\1235040010.py:64: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\zhaokangming\\AppData\\Local\\Temp\\ipykernel_8484\\1235040010.py:64: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "e:\\software\\anaconda3\\envs\\pytorch\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "e:\\software\\anaconda3\\envs\\pytorch\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "e:\\software\\anaconda3\\envs\\pytorch\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "e:\\software\\anaconda3\\envs\\pytorch\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "e:\\software\\anaconda3\\envs\\pytorch\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "e:\\software\\anaconda3\\envs\\pytorch\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "e:\\software\\anaconda3\\envs\\pytorch\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "e:\\software\\anaconda3\\envs\\pytorch\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "e:\\software\\anaconda3\\envs\\pytorch\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "e:\\software\\anaconda3\\envs\\pytorch\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "e:\\software\\anaconda3\\envs\\pytorch\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_results(train_losses, to_cpu(train_top1_accs), to_cpu(train_top5_accs), val_losses, to_cpu(val_top1_accs), to_cpu(val_top5_accs))\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
